Advancements in computational approaches for rapid metal site discovery in carbon-based materials for electrocatalysis

Heterogeneous electrocatalysts exhibit immense potential for advancing energy technologies. However, the constraints associated with noble metals have sparked a surge of interest in the exploration of single-atom catalysts and metal-carbon hybrids as alternative options. Designing metal sites in carbon-based materials has demonstrated high activity, selectivity, stability, and cost-effectiveness in various electrochemical reactions. In spite of these advantages, the intricate nature of the designed structures and the expansive design space encompassing potential metal site structures pose formidable challenges in terms of experimental characterization and optimization. To address these challenges, computational approaches have emerged as powerful tools to accelerate the discovery of new metal sites in carbon-based materials and understand the structure-catalytic property relationships for electrocatalysis. In this review paper, we provide an overview of the state-of-the-art computational approaches from reported modeled structures, theoretical foundations of computational methods in modeling electrochemical reactions, to the data-driven approaches to accelerate new catalyst design. We summarize the utilization of structure-binding energy relationships, virtual high-throughput screening methods, and machine learning techniques to explore a wide range of metal site structures and identify promising candidates for experimental validation. Furthermore, the review highlights the importance of considering the solvent effect and the impact of spin/oxidation states on extra electron transfer to enhance the accuracy of predicting binding energies. Finally, we summarize the current challenges and offer a brief perspective on future opportunities in the field of computational acceleration for carbon-based catalyst development. Computational approaches including quantum simulations, virtual high-throughput screening and machine learning accelerate the advance electrocatalysts discovery with proper accuracy and efficiency.